An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images
Authors
Keywords
COVID-19, Evolutionary computing, Soft computing, Intelligent systems, Deep learning, Hyperparameter tuning, Decision making
Journal
APPLIED SOFT COMPUTING
Volume 113, Issue -, Pages 107878
Publisher
Elsevier BV
Online
2021-09-08
DOI
10.1016/j.asoc.2021.107878
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images
- (2021) Fatih Demir APPLIED SOFT COMPUTING
- A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images
- (2021) Unais Sait et al. APPLIED SOFT COMPUTING
- Coronavirus Infections—More Than Just the Common Cold
- (2020) Catharine I. Paules et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
- (2020) Vikash Chouhan et al. Applied Sciences-Basel
- Profiling Early Humoral Response to Diagnose Novel Coronavirus Disease (COVID-19)
- (2020) Li Guo et al. CLINICAL INFECTIOUS DISEASES
- A machine learning forecasting model for COVID-19 pandemic in India
- (2020) R. Sujath et al. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
- A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization
- (2020) Majid Nour et al. APPLIED SOFT COMPUTING
- COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation
- (2020) Hoon Ko et al. JOURNAL OF MEDICAL INTERNET RESEARCH
- Can Lung US Help Critical Care Clinicians in the Early Diagnosis of Novel Coronavirus (COVID-19) Pneumonia?
- (2020) Erika Poggiali et al. RADIOLOGY
- MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks
- (2020) Omneya Attallah et al. PeerJ
- COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models
- (2020) S Dhamodharavadhani et al. Frontiers in Public Health
- A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans
- (2020) Shimaa El-bana et al. PeerJ Computer Science
- FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features
- (2020) Dina A. Ragab et al. PeerJ Computer Science
- CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection
- (2020) Muhammet Fatih Aslan et al. APPLIED SOFT COMPUTING
- An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare
- (2019) Okeke Stephen et al. Journal of Healthcare Engineering
- Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification
- (2019) Ivo M. Baltruschat et al. Scientific Reports
- Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
- (2019) Renlong Hang et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Barnacles Mating Optimizer: A new bio-inspired algorithm for solving engineering optimization problems
- (2019) Mohd Herwan Sulaiman et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Deep Convolutional Neural Networks for Chest Diseases Detection
- (2018) Rahib H. Abiyev et al. Journal of Healthcare Engineering
- Deep Recurrent Neural Networks for Hyperspectral Image Classification
- (2017) Lichao Mou et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now